Categorical regression models are used extensively to identify patterns of alcohol-related symptoms, assess administrative strategies, and identify medical and psychiatric conditions. However, many such models are inevitably misspecified which affects model validity and may lead to incorrect statistical inferences. Furthermore, currently available statistical software ignores the potential presence of model misspecification thus leaving the user with no automated means to readily assess model validity. Improved statistical measures or tests for evaluating the validity of categorical regression models would-be invaluable to alcohol-related research and the overall health care research community. Martingale Research will develop advanced statistical goodness-of-fit tests to evaluate the presence of model misspecification for categorical regression models. These tests will utilize asymptotic statistical theory to determine the presence of model misspecification (i.e., goodness-of- fit). The proposed Phase I study will demonstrate using a database representative of pre-existing NIAAA databases, that the goodness-of- fit tests are: 1) sensitive enough to detect and quantify the presence of model misspecification, and can 2) make reliable assessments of model fit. The results obtained in the Phase I study will demonstrate the essential technical feasibility required for further investigations into model validity research during Phase II and provide the foundation for developing commercially available software. PROPOSED COMMERCIAL APPLICATIONS: Martingale Research Corporation intends to incorporate the proposed goodness-of-fit tests into an advanced categorical regression software package that utilizes asymptotic theory to correctly handle model misspecification. This will provide improved solutions to a wide range of categorical problems encountered during: prediction of alcohol- related outcomes, medical diagnosis, financial forecasting, and information database interpretation and management.